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    Azure Machine Learning Engineering. Deploy, fine-tune, and optimize ML models using Microsoft Azure Sina Fakhraee, Balamurugan Balakreshnan, Megan Masanz

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Azure Machine Learning Engineering. Deploy, fine-tune, and optimize ML models using Microsoft Azure Sina Fakhraee, Balamurugan Balakreshnan, Megan Masanz - okladka książki

    Azure Machine Learning Engineering. Deploy, fine-tune, and optimize ML models using Microsoft Azure Sina Fakhraee, Balamurugan Balakreshnan, Megan Masanz - okladka książki

    Azure Machine Learning Engineering. Deploy, fine-tune, and optimize ML models using Microsoft Azure Sina Fakhraee, Balamurugan Balakreshnan, Megan Masanz - audiobook MP3

    Azure Machine Learning Engineering. Deploy, fine-tune, and optimize ML models using Microsoft Azure Sina Fakhraee, Balamurugan Balakreshnan, Megan Masanz - audiobook CD

    Autorzy:
    Sina Fakhraee, Balamurugan Balakreshnan, Megan Masanz
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    362
    Dostępne formaty:
         PDF
         ePub
    Data scientists working on productionizing machine learning (ML) workloads face a breadth of challenges at every step owing to the countless factors involved in getting ML models deployed and running. This book offers solutions to common issues, detailed explanations of essential concepts, and step-by-step instructions to productionize ML workloads using the Azure Machine Learning service. You’ll see how data scientists and ML engineers working with Microsoft Azure can train and deploy ML models at scale by putting their knowledge to work with this practical guide.
    Throughout the book, you’ll learn how to train, register, and productionize ML models by making use of the power of the Azure Machine Learning service. You’ll get to grips with scoring models in real time and batch, explaining models to earn business trust, mitigating model bias, and developing solutions using an MLOps framework.
    By the end of this Azure Machine Learning book, you’ll be ready to build and deploy end-to-end ML solutions into a production system using the Azure Machine Learning service for real-time scenarios.

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    O autorach książki

    Sina Fakhraee, Ph.D., is currently working at Microsoft as an enterprise data scientist and senior cloud solution architect. He has helped customers to successfully migrate to Azure by providing best practices around data and AI architectural design and by helping them implement AI/ML solutions on Azure. Prior to working at Microsoft, Sina worked at Ford Motor Company as a product owner for Ford’s AI/ML platform. Sina holds a Ph.D. degree in computer science and engineering from Wayne State University and prior to joining the industry, he taught various undergrad and grad computer science courses part time.
    Balamurugan Balakreshnan is a principal cloud solution architect at Microsoft Data/AI Architect and Data Science. He has provided leadership on digital transformations with AI and cloud-based digital solutions. He has also provided leadership in terms of ML, the IoT, big data, and advanced analytical solutions.
    Megan Masanz is a principal cloud solution architect at Microsoft focused on data, AI, and data science, passionately enabling organizations to address business challenges through the establishment of strategies and road maps for the planning, design, and deployment of Azure Cloud-based solutions. Megan is adept at paving the path to data science via computer science given her master’s in computer science with a focus on data science.

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